Large language model-based question answering method

Abstract

A method includes: obtaining a document comprising at least one page for question answering; determining a first vector corresponding to each of the at least one page; determining a second vector corresponding to a target question text to be answered; performing the following first operations: determining, based on the second vector and the first vector corresponding to each of the at least one page, a first similarity between the target question text and each of the at least one page; determining, based on the first similarities, at least one candidate page with the highest similarity to the target question text among the at least one page; and generating, based on the at least one candidate page and the target question text, a first identifier and first content, or second identifier and second content, using a large language model.

1 . A large model-based question answering method, comprising:
 obtaining a document for question answering, wherein the document comprises at least one page;   determining a first vector corresponding to each of the at least one page;   obtaining a target question text to be answered to determine a second vector corresponding to the target question text;   performing the following first operations on the target question text:
 determining, based on the second vector and the first vector corresponding to each of the at least one page, a first similarity between the target question text and each of the at least one page; 
 determining, based on the first similarities, at least one candidate page with the highest similarity to the target question text among the at least one page; and 
 generating, based on the at least one candidate page and the target question text, a first identifier and first content, or generating a second identifier and second content, using a large language model, 
 wherein the first content comprises an answer corresponding to the target question text, wherein the answer is generated based on the at least one candidate page, and the first identifier is used to identify that the content within the at least one candidate page is sufficient to answer the target question text; 
 wherein the second content comprises at least two sub-question texts corresponding to the target question text, wherein the second identifier is used to identify that the content within the at least one candidate page is insufficient to answer the target question text, wherein the at least two sub-question texts respectively correspond to sub-steps for answering the target question text, wherein the at least two sub-question texts are used to obtain the answer to the target question text. 
   
     
     
         2 . The method of  claim 1 , wherein the first vector is a first vector matrix, and each row or column of the first vector matrix serves as a third vector respectively, wherein each third vector is used to represent at least a part of the page content within the page, wherein determining, based on the second vector and the first vector, the first similarity between the target question text and each of the at least one page comprises:
 for each of the at least one page, determining, among all the third vectors corresponding to the page, the third vector with the highest similarity to the second vector, and a second similarity between the third vector with the highest similarity and the second vector; and   determining the first similarity between the target question text and each of the at least one page based on the second similarity respectively.   
     
     
         3 . The method of  claim 2 , wherein the second vector is a second vector matrix, and each row or column of the second vector matrix serves as a fourth vector respectively, wherein each fourth vector is used to represent at least a part of the text content within the target question text, wherein,
 determining, based on the second vector and the first vector, the first similarity between the target question text and each of the at least one page comprises:   for each fourth vector in the second vector matrix, determining, among all the third vectors, the third vector with the highest similarity to the fourth vector, and a third similarity between the third vector with the highest similarity and the fourth vector; and   determining, based on the third similarity corresponding to each of the fourth vectors, the first similarity between the target question text and each of the at least one page.   
     
     
         4 . The method of  claim 1 , wherein determining the first vector corresponding to each of the at least one page comprises:
 for each of the at least one page, inputting the page image of the page into a pre-trained visual language model to obtain the first vector corresponding to the page.   
     
     
         5 . The method of  claim 2 , wherein determining the first vector corresponding to each of the at least one page comprises: for each of the at least one page, performing the following operations:
 segmenting the page into a plurality of page blocks, wherein each of the plurality of page blocks corresponds to at least a part of the page content in the page; and   determining, for each page block, a third vector corresponding to the page block to generate the first vector corresponding to the page based on the third vectors corresponding to the plurality of page blocks.   
     
     
         6 . The method of  claim 1 , wherein obtaining the target question text to be answered to determine the second vector corresponding to the target question text comprises:
 inputting the target question text into a pre-trained visual language model to obtain the second vector corresponding to the target question text.   
     
     
         7 . The method of  claim 3 , wherein obtaining the target question text to be answered to determine the second vector corresponding to the target question text comprises:
 performing word segmentation on the target question text to obtain a plurality of words corresponding to the target question text;   determining a fourth vector corresponding to each of the plurality of words to generate the second vector corresponding to the target question text based on the fourth vectors corresponding to the plurality of words.   
     
     
         8 . The method of  claim 1 , further comprising:
 in response to obtaining the second identifier and second content, for each of the at least two sub-question texts, performing the first operations on the sub-question text as a new target question text to obtain the first identifier and first content corresponding to the new target question text, or to obtain the second identifier and second content corresponding to the new target question text;   in response to obtaining the first identifier and first content corresponding to each sub-question text of the target question text, generating the answer to the target question text using a large language model based on the first content corresponding to each sub-question text of the target question text.   
     
     
         9 . The method of  claim 8 , further comprising:
 in response to obtaining the second identifier and second content corresponding to a first sub-question text, performing the first operations on each sub-question text of the first sub-question text as a new target question text to obtain the first identifier and first content corresponding to the new target question text, or to obtain the second identifier and second content corresponding to the new target question text;   in response to obtaining the first identifier and first content corresponding to each sub-question text of the first sub-question text, generating, based on the first content corresponding to each sub-question text of the first sub-question text, the answer to the first sub-question text using a large language model; and   generating, based at least on the answer to the first sub-question text and the answer corresponding to a second sub-question text, the answer to the target question text using a large language model,   wherein the first sub-question text is at least one of the at least two sub-question texts corresponding to the target question text, and the second sub-question text is the other sub-question text of the at least two sub-question texts corresponding to the target question text other than the first sub-question text.   
     
     
         10 . The method of  claim 1 , wherein the first content further comprises first inference information, wherein the first inference information is used to characterize the inference process of the large language model when generating the first identifier and the first content. 
     
     
         11 . The method of  claim 1 , wherein the second content further comprises second inference information, wherein the second inference information is used to characterize the inference process of the large language model when generating the second identifier and the second content. 
     
     
         12 . The method of  claim 1 , further comprising:
 generating, based on the target question text and the first content, at least one rewritten question text corresponding to the target question text using a large language model;   performing the first operations on each rewritten question text as a new target question text to obtain the first identifier and first content corresponding to the new target question text, or to obtain the second identifier and second content corresponding to the new target question text;   in response to obtaining the first identifier and first content corresponding to each rewritten question text, generating, based on the first content corresponding to each rewritten question text and the first content corresponding to the target question text, new first content corresponding to the target question text using a large language model, to obtain the answer to the target question text based on the new first content.   
     
     
         13 . The method of  claim 12 , further comprising:
 in response to obtaining the second identifier and second content corresponding to a third question text, performing the first operations on each sub-question text of the third question text as a new target question text to obtain the first identifier and first content corresponding to the new target question text, or to obtain the second identifier and second content corresponding to the new target question text;   in response to obtaining the first identifier and first content corresponding to each sub-question text of the third question text, generating, based on the first content corresponding to each sub-question text of the third question text, the answer to the third question text using a large language model; and   generating, based at least on the answer to the third question text and the answer corresponding to a fourth question text, the answer to the target question text using a large language model;   wherein the third question text is at least one question text of the at least one rewritten question text, and the fourth question text is another question text of the at least one rewritten question text other than the third question text.   
     
     
         14 . An electronic device, comprising:
 a memory storing one or more programs configured to be executed by one or more processors, the one or more programs including instructions for performing operations comprising:   obtaining a document for question answering, wherein the document comprises at least one page;   determining a first vector corresponding to each of the at least one page;   obtaining a target question text to be answered to determine a second vector corresponding to the target question text;   performing the following first operations on the target question text:
 determining, based on the second vector and the first vector corresponding to each of the at least one page, a first similarity between the target question text and each of the at least one page; 
 determining, based on the first similarities, at least one candidate page with the highest similarity to the target question text among the at least one page; and 
 generating, based on the at least one candidate page and the target question text, a first identifier and first content, or generating a second identifier and second content, using a large language model, 
 wherein the first content comprises an answer corresponding to the target question text, wherein the answer is generated based on the at least one candidate page, and the first Identifier is used to identify that the content within the at least one candidate page is sufficient to answer the target question text; 
   wherein the second content comprises at least two sub-question texts corresponding to the target question text, wherein the second identifier is used to identify that the content within the at least one candidate page is insufficient to answer the target question text, wherein the at least two sub-question texts respectively correspond to sub-steps for answering the target question text, wherein the at least two sub-question texts are used to obtain the answer to the target question text.   
     
     
         15 . The electronic device of  claim 14 , wherein the first vector is a first vector matrix, and each row or column of the first vector matrix serves as a third vector respectively, wherein each third vector is used to represent at least a part of the page content within the page, wherein determining, based on the second vector and the first vector, the first similarity between the target question text and each of the at least one page comprises:
 for each of the at least one page, determining, among all the third vectors corresponding to the page, the third vector with the highest similarity to the second vector, and a second similarity between the third vector with the highest similarity and the second vector; and 
 determining the first similarity between the target question text and each of the at least one page based on the second similarity respectively. 
 
     
     
         16 . The electronic device of  claim 15 , wherein the second vector is a second vector matrix, and each row or column of the second vector matrix serves as a fourth vector respectively, wherein each fourth vector is used to represent at least a part of the text content within the target question text, wherein,
 determining, based on the second vector and the first vector, the first similarity between the target question text and each of the at least one page comprises:   for each fourth vector in the second vector matrix, determining, among all the third vectors, the third vector with the highest similarity to the fourth vector, and a third similarity between the third vector with the highest similarity and the fourth vector; and   determining, based on the third similarity corresponding to each of the fourth vectors, the first similarity between the target question text and each of the at least one page.   
     
     
         17 . The electronic device of  claim 14 , the operations further comprising:
 in response to obtaining the second identifier and second content, for each of the at least two sub-question texts, performing the first operations on the sub-question text as a new target question text to obtain the first identifier and first content corresponding to the new target question text, or to obtain the second identifier and second content corresponding to the new target question text;   in response to obtaining the first identifier and first content corresponding to each sub-question text of the target question text, generating the answer to the target question text using a large language model based on the first content corresponding to each sub-question text of the target question text.   
     
     
         18 . The electronic device of  claim 17 , the operations further comprising:
 in response to obtaining the second identifier and second content corresponding to a first sub-question text, performing the first operations on each sub-question text of the first sub-question text as a new target question text to obtain the first identifier and first content corresponding to the new target question text, or to obtain the second identifier and second content corresponding to the new target question text;   in response to obtaining the first identifier and first content corresponding to each sub-question text of the first sub-question text, generating, based on the first content corresponding to each sub-question text of the first sub-question text, the answer to the first sub-question text using a large language model; and   generating, based at least on the answer to the first sub-question text and the answer corresponding to a second sub-question text, the answer to the target question text using a large language model,   wherein the first sub-question text is at least one of the at least two sub-question texts corresponding to the target question text, and the second sub-question text is the other sub-question text of the at least two sub-question texts corresponding to the target question text other than the first sub-question text.   
     
     
         19 . The electronic device of  claim 14 , the operations further comprising:
 generating, based on the target question text and the first content, at least one rewritten question text corresponding to the target question text using a large language model;   performing the first operations on each rewritten question text as a new target question text to obtain the first identifier and first content corresponding to the new target question text, or to obtain the second identifier and second content corresponding to the new target question text;   in response to obtaining the first identifier and first content corresponding to each rewritten question text, generating, based on the first content corresponding to each rewritten question text and the first content corresponding to the target question text, new first content corresponding to the target question text using a large language model, to obtain the answer to the target question text based on the new first content;   in response to obtaining the second identifier and second content corresponding to a third question text, performing the first operations on each sub-question text of the third question text as a new target question text to obtain the first identifier and first content corresponding to the new target question text, or to obtain the second identifier and second content corresponding to the new target question text;   in response to obtaining the first identifier and first content corresponding to each sub-question text of the third question text, generating, based on the first content corresponding to each sub-question text of the third question text, the answer to the third question text using a large language model; and   generating, based at least on the answer to the third question text and the answer corresponding to a fourth question text, the answer to the target question text using a large language model;   wherein the third question text is at least one question text of the at least one rewritten question text, and the fourth question text is another question text of the at least one rewritten question text other than the third question text.   
     
     
         20 . A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to perform the following operations:
 obtaining a document for question answering, wherein the document comprises at least one page;   determining a first vector corresponding to each of the at least one page;   obtaining a target question text to be answered to determine a second vector corresponding to the target question text;   performing the following first operations on the target question text:
 determining, based on the second vector and the first vector corresponding to each of the at least one page, a first similarity between the target question text and each of the at least one page; 
 determining, based on the first similarities, at least one candidate page with the highest similarity to the target question text among the at least one page; and 
 generating, based on the at least one candidate page and the target question text, a first identifier and first content, or generating a second identifier and second content, using a large language model, 
 wherein the first content comprises an answer corresponding to the target question text, wherein the answer is generated based on the at least one candidate page, and the first identifier is used to identify that the content within the at least one candidate page is sufficient to answer the target question text; 
   wherein the second content comprises at least two sub-question texts corresponding to the target question text, wherein the second identifier is used to identify that the content within the at least one candidate page is insufficient to answer the target question text, wherein the at least two sub-question texts respectively correspond to sub-steps for answering the target question text, wherein the at least two sub-question texts are used to obtain the answer to the target question text.